Case studies are made to investigate the relationship between the accuracy of energy production estimation and project feasibility indicators such as rate of return on equity (ROE) and debt service coverage ratio (DSCR) for three wind farm projects. It is found out that 1% improvement in the accuracy of energy production estimation may enhance the ROE by more than 0.5% in the case of P95, thanks to improved financing terms. AHP survey shows that MCP correlation of measured in situ wind data with long term wind speed distribution and hands-on experiences of flow analysis are more important than other factors for more precise annual energy production estimation.

Small and medium-sized enterprises (SMEs) constitute an important part of current industrial economies. Information technologies can be useful strategic weapons for SMEs by enhancing their competitiveness. Categorized as one kind of cloud computing, SaaS is a computing resource and software sharing model which can be accessed via the Internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level in SMEs. This study attempts to identify the determinants of SaaS adoption intention among Korean SMEs. Through the lens of the theory of planned behavior, this study adopts technological, organizational, environmental factors to explore the determinants of cloud computing adoption intention. The research population is the SMEs that have been funded by the Korean government. Partial least square method was used for empirical analysis of 190 samples collected through on-line survey. The results show that the positive attitude is influenced by business process improvements. Vender support and top management support are positively associated with subjective norm. Vendor support, top management support can relieve perceived behavior control factors. Government support directly influences adoption intention of cloud computing. These findings can provide useful strategy for both SMEs and vendors of SaaS.

The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.

With the recent introduction of supply chain management (SCM), quality management has extended from within companies to between companies. As a result, supply chain quality management (SCQM) has received increased attention. However, existing SCQM studies only focus on what impact quality control in supply chains have on company performance while virtually no studies examine quality control efficiencies. This paper, therefore, evaluated the SCQM efficiency of a parent company and its partner companies by using Data Envelopment Analysis (DEA) based on the Quality Collaboration Index for Supply Chain Management (QCI-SCM) conducted by the Korean Standards Association for its `Quality Innovation-Based Building and Expansion of Business.` Study results showed that a parent company and its partners showed an overall average efficiency of approximately 80% (parent company 80.37%, partner company 79.05%). By also performing a discriminant analysis based on the calculated efficiency scores using DEA, factors that made companies efficient or inefficient were different between the two groups. In parent companies, efficiency and inefficiency were determined by factors such as communication, infra-structure, support, delivery of quality, and benefit sharing, whereas in partner companies, talent development, infrastructure, crisis management, and delivery of quality were the determining factors. In this paper, we examined the efficiency of SCQM and analyzed them from the perspective of both the parent company and partner companies to offer strategic SCQM insights.

Recently, many companies are interested in adopting cloud computing as their IT strategy. However, no distinct results have appeared in the substantial implementation of this technology. The main reason for such result is from the absence of research models leading to high impact studies on cloud computing. Thus, this study attempts to find a possible answer for the following research question : what factors influence an organizational assimilation of cloud computing? This study investigates Technology-Push (TP)/Need-Pull (NP) theory as a main factor affecting cloud computing assimilation. Also, the study examines the moderating role of organizational readiness. TP includes of perceived benefits, vendor pressure, cost savings, and IT activity intensity while NP includes competitor orientation, information technology policy, technological turbulence, and performance gaps. In addition, organizational readiness suggests two variables, financial resources and technological knowledge. Result from 217 adopting organizations showed that all of these factors with exception of competitor orientation and vendor pressure, have statistically significant impact on assimilation of cloud computing. The implications of the findings propose a theoretical framework for the foundation of studies on cloud computing assimilation, which can server as important practical guidelines for technology development.

In this study, we empirically examine the impact of win-win growth effort of domestic large firms on their financial performance. Specifically, we classify the financial performance into three aspects such as profitability, stability and efficiency, select corresponding financial ratios to each aspect, and analyze the causal relationship between the firms` win-win growth effort and each of the financial ratios. In addition, we figure out the impact of the firms` win-win growth effort on their stock rate of return. From the analysis, we show that the win-win growth effort has a positive impact on the firms` profitability, stability and stock prices; however, it does not give statistically significant impact on the firms` efficiency with even negative impact on it. These results imply that the firms` win-win growth effort could bring about inefficiency in their business operations, but the effort could increase the firms` profitability and make their financial structure more stable. Furthermore, the effort could enhance the firms` image of leading CSR (corporate social responsibility), which in turn increase their stock values.

Of the new and renewable energies currently being pursued domestically, wind energy, together with solar photovoltaic energy, is a new core growth driver industry of Korea. As of May 2012, 33 wind farms at a capacity of 347.8MW are in operation domestically. The purpose of this study was to compare and analyze how efficiently each operational wind farm is utilizing its power generation capacity and the weather resource of wind. For this purpose, the study proceeded in 3 phases. In phase 1, ANOVA analysis was performed for each wind farm, thereby categorizing farms according to capacity, region, generator manufacturer, and quantity of weather resources available and comparing and analyzing the differences among their operating efficiency. In phase 2, for comparative analysis of the operating efficiency of each farm, Data Envelopment Analysis (DEA) was used to calculate the efficiency index of individual farms. In the final phase, phase 3, regression analysis was used to analyze the effects of weather resources and the operating efficiency of the wind farm on the power generation per unit equipment. Results shows that for wind power generation, only a few farms had relatively high levels of operating efficiency, with most having low efficiency. Regression analysis showed that for wind farms, a 1 hour increase in wind speeds of at least 3m/s resulted in an average increase of 0.0000045MWh in power generation per 1MW generator equipment capacity, and a unit increase in the efficiency scale was found to result in approximately 0.20MWh power generation improvement per unit equipment.

Climate changes and environmental pollution recently became a matter of global interest. Korean government established low carbon green growth act in the light of international environment regulation and started demonstration certificate project for GMS (Green Management System). We aim to explore audit data resulted from demonstration companies that pursued the GMS certificate. The demonstration companies are consisted of 11 companies that a certification body L gave the certificate. The audit data results were formed by minor nonconformities detected in the field evaluation based on GMS standards, KS I 7001/2 : 2011. We found out significant differences for minor nonconformities between types of industry and between major clauses of Part 1 and Part 2 in GMS standards. We make an effort to figure out the implication of causes of the significant differences. These results are expected to contribute to understand GMS operation situations and are utilized as a reference for energy management, social responsibility, and green gas reduction.

The objective of this paper is to provide an improved technology appraisal model, which considers a variety of macroeconomic variables such as consumer price index and producer price index. The improved model was built using cross correlation analysis and logistic regression analysis. The AUROC analysis showed that goodness-of-fit of the proposed model turned out to be improved than that of the existing model. The model proposed in the paper would be helpful for making a reasonable investments and financing decision, lessening the default rates by systematic risk management, and enhancing the technology commercialization capabilities.

In this paper, we considers the outsourcing decision problem in a single machine scheduling problem. The decision problem is to determine for each job whether to be processed on an in-house manufacturing or external facilities(outsourcing). Moreover, this paper considers a situation where each job has a due date. The objective of the problem is to minimize the outsourcing cost, subject to the due date constraints. The considered problem is proved to be NP-hard. Some solution properties and valid inequalities are derived, and an effective lower bound is derived based on the LP-relaxation. The results of experimental tests are presented to evaluate the performance of the suggested lower bound.